Between Policy and Practice: GenAI Adoption in Agile Software Development Teams
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912817402085376 |
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| author | Neumann, Michael Bischof, Lasse Hinz, Nic Elias Stockmann, Luca Schrader, Dennis Ahaus, Ana Carolina Demirci, Erim Can Gabel, Benjamin Rauschenberger, Maria Diebold, Philipp Fritzemeier, Henning Przybylek, Adam |
| author_facet | Neumann, Michael Bischof, Lasse Hinz, Nic Elias Stockmann, Luca Schrader, Dennis Ahaus, Ana Carolina Demirci, Erim Can Gabel, Benjamin Rauschenberger, Maria Diebold, Philipp Fritzemeier, Henning Przybylek, Adam |
| contents | Context: The rapid emergence of generative AI (GenAI) tools has begun to reshape various software engineering activities. Yet, their adoption within agile environments remains underexplored. Objective: This study investigates how agile practitioners adopt GenAI tools in real-world organizational contexts, focusing on regulatory conditions, use cases, benefits, and barriers. Method: An exploratory multiple case study was conducted in three German organizations, involving 17 semi-structured interviews and document analysis. A cross-case thematic analysis was applied to identify GenAI adoption patterns. Results: Findings reveal that GenAI is primarily used for creative tasks, documentation, and code assistance. Benefits include efficiency gains and enhanced creativity, while barriers relate to data privacy, validation effort, and lack of governance. Using the Technology-Organization-Environment (TOE) framework, we find that these barriers stem from misalignments across the three dimensions. Regulatory pressures are often translated into policies without accounting for actual technological usage patterns or organizational constraints. This leads to systematic gaps between policy and practice. Conclusion: GenAI offers significant potential to augment agile roles but requires alignment across TOE dimensions, including clear policies, data protection measures, and user training to ensure responsible and effective integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07051 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Between Policy and Practice: GenAI Adoption in Agile Software Development Teams Neumann, Michael Bischof, Lasse Hinz, Nic Elias Stockmann, Luca Schrader, Dennis Ahaus, Ana Carolina Demirci, Erim Can Gabel, Benjamin Rauschenberger, Maria Diebold, Philipp Fritzemeier, Henning Przybylek, Adam Software Engineering Context: The rapid emergence of generative AI (GenAI) tools has begun to reshape various software engineering activities. Yet, their adoption within agile environments remains underexplored. Objective: This study investigates how agile practitioners adopt GenAI tools in real-world organizational contexts, focusing on regulatory conditions, use cases, benefits, and barriers. Method: An exploratory multiple case study was conducted in three German organizations, involving 17 semi-structured interviews and document analysis. A cross-case thematic analysis was applied to identify GenAI adoption patterns. Results: Findings reveal that GenAI is primarily used for creative tasks, documentation, and code assistance. Benefits include efficiency gains and enhanced creativity, while barriers relate to data privacy, validation effort, and lack of governance. Using the Technology-Organization-Environment (TOE) framework, we find that these barriers stem from misalignments across the three dimensions. Regulatory pressures are often translated into policies without accounting for actual technological usage patterns or organizational constraints. This leads to systematic gaps between policy and practice. Conclusion: GenAI offers significant potential to augment agile roles but requires alignment across TOE dimensions, including clear policies, data protection measures, and user training to ensure responsible and effective integration. |
| title | Between Policy and Practice: GenAI Adoption in Agile Software Development Teams |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2601.07051 |